<p>Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.</p>
Gross primary productivity of Brazilian Savanna (Cerrado) estimated by different remote sensing-based models
